@InProceedings{hua-wang:2018:Long,
author = {Hua, Xinyu and Wang, Lu},
title = {Neural Argument Generation Augmented with Externally Retrieved Evidence},
booktitle = {Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
month = {July},
year = {2018},
address = {Melbourne, Australia},
publisher = {Association for Computational Linguistics},
abstract = {
High quality arguments are essential elements for human reasoning and decision-making processes. However, effective argument construction is a challenging task for both human and machines. In this work, we study a novel task on automatically generating arguments of a different stance for a given statement. We propose an encoder-decoder style neural network-based argument generation model enriched with externally retrieved evidence from Wikipedia. Our model first generates a set of talking point phrases as intermediate representation, followed by a separate decoder producing the final argument based on both input and the keyphrases.
}
}